case study: vertical intelligence in banking

Enabling AI in Banking

% Improved Churn Prediction

Optimize ROI across retention and acquisition activities.

Using AI to Predict Customer Churn in Commercial Banking

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Executive Summary

This case study examines how NowVertical Group implemented AI solutions for ICBC, the largest bank in China, to predict customer churn in commercial banking. By leveraging advanced AI techniques like selective filtering, logistic regression, and clustering, ICBC improved its ability to identify customers at risk of churning. The implementation resulted in a churn identification effectiveness improvement of over 15%, allowing ICBC to take proactive measures for customer retention.

The benefits of predicting churn in commercial banking include proactive customer retention, enhanced satisfaction, revenue optimization, cost reduction, and gaining a competitive advantage. These outcomes empower banks to improve operations, foster long-term customer relationships, and drive growth in the industry.

Business needs

The Industrial and Commercial Bank of China Limited (ICBC) needed to improve potential churn identification. By accurately predicting churn, ICBC aimed to take proactive measures to retain customers and mitigate the negative impacts of customer attrition.

Business results after AI implementation:

  • Churn identification effectiveness improvement of greater than 15%
  • Better ability to proactively implement customer retention measures
  • Cost reduction by focusing on retention rather than customer acquisition
  • Ability to utilize data-driven decision making
  • Opportunity to improve operational efficiency